Explainable prediction of N-V-related defects in nanodiamond using neural networks and Shapley values

Date

2022-01-19

Authors

Barnard, Amanda

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

Abstract

Although the negatively charged nitrogen-vacancy (N-V ) defect in nanodiamonds is desirable for a variety of biomedical applications, a range of other defect complexes involving nitrogen and/or vacancies can also exist, depending on their relative stability. Using machine learning, a re-usable model is developed to predict the likelihood of a particular defect complex being stable at a given depth below reconstructed or hydrogen-passivated surfaces. A neural network is used to generate a system of equations that can be easily implemented in any workflow, and explainable artificial intelligence (XAI) methods are used to provide insights into which structural features and defect configurations are most responsible for the model prediction. It is found that, although the number of nitrogen atoms present in the defect is the most important feature determining the defect likelihood, the most influential data instances are the unlikely defects, providing a type of baseline for comparison.

Description

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Citation

Barnard, Explainable prediction of N-V-related defects in nanodiamond using neural networks and Shapley values, Cell Reports Physical Science (2021), https://doi.org/10.1016/j.xcrp.2021.100696

Source

Cell Reports Physical Science

Type

Journal article

Book Title

Entity type

Access Statement

Open Access

License Rights

Creative Commons BY-NC-ND license

DOI

10.1016/j.xcrp.2021.100696

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